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Articles 1 - 6 of 6
Full-Text Articles in Social and Behavioral Sciences
Multi-View Indoor Scene Reconstruction From Compressed Through-Wall Radar Measurements Using A Joint Bayesian Sparse Representation, Van Ha Tang, Abdesselam Bouzerdoum, Son Lam Phung, Fok Hing Chi Tivive
Multi-View Indoor Scene Reconstruction From Compressed Through-Wall Radar Measurements Using A Joint Bayesian Sparse Representation, Van Ha Tang, Abdesselam Bouzerdoum, Son Lam Phung, Fok Hing Chi Tivive
Faculty of Engineering and Information Sciences - Papers: Part A
This paper addresses the problem of scene reconstruction, incorporating wall-clutter mitigation, for compressed multi-view through-the-wall radar imaging. We consider the problem where the scene is sensed using different reduced sets of frequencies at different antennas. A joint Bayesian sparse recovery framework is first employed to estimate the antenna signal coefficients simultaneously, by exploiting the sparsity and correlations between antenna signals. Following joint signal coefficient estimation, a subspace projection technique is applied to segregate the target coefficients from the wall contributions. Furthermore, a multitask linear model is developed to relate the target coefficients to the scene, and a composite scene image …
Functional Brain Network Classification With Compact Representation Of Sice Matrices, Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li
Functional Brain Network Classification With Compact Representation Of Sice Matrices, Jianjia Zhang, Luping Zhou, Lei Wang, Wanqing Li
Faculty of Engineering and Information Sciences - Papers: Part A
Recently, sparse inverse covariance estimation (SICE) technique has been employed to model functional brain connectivity. The inverse covariance matrix (SICE matrix in short) estimated for each subject is used as a representation of brain connectivity to discriminate Alzheimers disease from normal controls. However, we observed that direct use of the SICE matrix does not necessarily give satisfying discrimination, due to its high dimensionality and the scarcity of training subjects. Looking into this problem, we argue that the intrinsic dimensionality of these SICE matrices shall be much lower, considering i) an SICE matrix resides on a Riemannian manifold of symmetric positive …
Mining Mid-Level Features For Action Recognition Based On Effective Skeleton Representation, Pichao Wang, Wanqing Li, Philip O. Ogunbona, Zhimin Gao, Hanling Zhang
Mining Mid-Level Features For Action Recognition Based On Effective Skeleton Representation, Pichao Wang, Wanqing Li, Philip O. Ogunbona, Zhimin Gao, Hanling Zhang
Faculty of Engineering and Information Sciences - Papers: Part A
Recently, mid-level features have shown promising performance in computer vision. Mid-level features learned by incorporating class-level information are potentially more discriminative than traditional low-level local features. In this paper, an effective method is proposed to extract mid-level features from Kinect skeletons for 3D human action recognition. Firstly, the orientations of limbs connected by two skeleton joints are computed and each orientation is encoded into one of the 27 states indicating the spatial relationship of the joints. Secondly, limbs are combined into parts and the limb's states are mapped into part states. Finally, frequent pattern mining is employed to mine the …
Sparse Representation Of Gpr Traces With Application To Signal Classification, Wenbin Shao, Abdesselam Bouzerdoum, Son Lam Phung
Sparse Representation Of Gpr Traces With Application To Signal Classification, Wenbin Shao, Abdesselam Bouzerdoum, Son Lam Phung
Faculty of Engineering and Information Sciences - Papers: Part A
Sparse representation (SR) models a signal with a small number of elementary waves using an overcomplete dictionary. It has been employed for a wide range of signal and image processing applications, including denoising, deblurring, and compression. In this paper, we present an adaptive SR method for modeling and classifying ground penetrating radar (GPR) signals. The proposed method decomposes each GPR trace into elementary waves using an adaptive Gabor dictionary. The sparse decomposition is used to extract salient features for SR and classification of GPR signals. Experimental results on real-world data show that the proposed sparse decomposition achieves efficient signal representation …
A State-Based Knowledge Representation Approach For Information Logical Inconsistency Detection In Warning Systems, Jun Ma, Guangquan Zhang, Jie Lu
A State-Based Knowledge Representation Approach For Information Logical Inconsistency Detection In Warning Systems, Jun Ma, Guangquan Zhang, Jie Lu
Faculty of Engineering and Information Sciences - Papers: Part A
Detecting logical inconsistency in collected information is a vital function when deploying a knowledge-based warning system to monitor a specific application domain for the reason that logical inconsistency is often hidden from seemingly consistent information and may lead to unexpected results. Existing logical inconsistency detection methods usually focus on information stored in a knowledge base by using a well-defined general purpose knowledge representation approach, and therefore cannot fulfill the demands of a domain-specific situation. This paper first proposes a state-based knowledge representation approach, in which domain-specific knowledge is expressed by combinations of the relevant objects' states. Based on this approach, …
The Zeckendorf Representation And The Golden Sequence, Martin Bunder, Keith Tognetti
The Zeckendorf Representation And The Golden Sequence, Martin Bunder, Keith Tognetti
Faculty of Engineering and Information Sciences - Papers: Part A
The Zeckendorf representation of a number is simply the representation of that number as the sum of distinct Fibonacci numbers. If the number of terms of this sum is minimized, that representation is unique, as also is the representation when the number of terms is maximized.